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Epidemiology
Original research
Cross-sectional and longitudinal analyses of variations of anthropometric measures of obesity with age in the Jackson Heart Study
  1. Yan Gao1,
  2. Yuan-I Min1,
  3. Ronny A Bell2,
  4. Alain G Bertoni3,
  5. Adolfo Correa1
  1. 1Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
  2. 2Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  3. 3Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  1. Correspondence to Dr Yan Gao; yangao0311{at}yahoo.com

Abstract

Objective To explore how anthropometric measures of obesity vary with age among African American (AA) adults.

Participants and setting 3634 AA adults participated in the Jackson Heart Study (Jackson, Mississippi, USA) from 2004 to 2013.

Outcome measures Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR).

Methods Linear regression models were used to estimate the mean differences in anthropometric measures cross-sectionally by age group. Longitudinal changes in anthropometric measures over time (ie, the ageing effect) within each sex and age group were analysed using mixed effects models. All regression models were adjusted for education and lifestyle factors.

Results In cross-sectional analysis, older age was associated with lower BMI, WC and WHtR, but higher WHR in both sexes. Compared with 25 to <44 years age group, the mean (95% CI) BMI, WC and WHtR was 0.80 (0.66 to 0.94), 0.27 (0.13 to 0.42) and 0.18 (0.03 to 0.32) standardised (SD) unit lower, while WHR was 0.48 (0.33 to 0.62) SD unit higher in the 75+ years age group. In longitudinal analysis, ageing was associated with increased BMI, WC and WHtR, among younger age groups but not in older age groups. However, WHR tended to increase with ageing across all age groups in both sexes. Among men 75+ years old, the mean change (95% CI) in BMI, WC and WHtR for every 5 years increase in age, was –0.20 (–0.29 to –0.11), –0.19 (−0.31 to –0.07), –0.15 (−0.27 to –0.02) SD unit, respectively, while it was 0.24 (0.05 to 0.44) SD unit for WHR.

Conclusions Among middle-aged AA adults, all four anthropometric measures of obesity examined increased with ageing. However, among elderly AA adults, only WHR showed continued increase with ageing. WHR may be a better anthropometric measure for monitoring obesity in older AA adults.

  • EPIDEMIOLOGY
  • PUBLIC HEALTH
  • Aging
  • Obesity

Data availability statement

Data are available upon reasonable request. As a National Heart, Lung, and Blood Institute (NHLBI)-funded study, the Jackson Heart Study (JHS) follows the NHLBI’s policy for data sharing, which includes depositing the data into the NHLBI’s Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) to make it publicly available to other investigators. The link to the JHS data set: https://biolincc-nhlbi-nih-gov.ezproxy.u-pec.fr/studies/jhs/. Download directions are provided on the website. NHLBI further requires the JHS to maintain the accuracy of this data set, so the JHS Coordinating Center sends modifications to BioLINCC for incorporation as appropriate. Privacy and access settings are controlled by BioLINCC, and the investigators have no influence on these settings.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi-org.ezproxy.u-pec.fr/10.1136/bmjopen-2022-069127

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    Strengths and limitations of this study

    • The strengths of this study include the large sample size and longitudinal follow-up of a well-characterised cohort of African American (AA) adults.

    • The longitudinal change in anthropometric measures over the life-course were derived from participants from different birth cohorts.

    • The anthropometric measures from V2 and V3 were used for the analysis, while the covariates used for the analysis were collected at V1.

    • The results of this study may not be generalisable to all AA adults across the USA.

    Introduction

    Ongoing monitoring of trends in obesity is a global health priority due to increased prevalence of obesity worldwide and its associated morbidity and mortality, transgenerational effects and disproportionate burden in disadvantaged populations.1–4 Reports of variations in prevalence of obesity by age, sex and race/ethnicity are helpful for obesity-related risk stratification as the body distribution of adiposity appears to vary by demographic characteristics.5 However, to date, the literature on trends in obesity by demographic characteristics has focused on age-variations and sex-variations in prevalence of obesity defined by one anthropometric measure only (eg, body mass index (BMI), waist circumference (WC) or waist-to-height ratio (WHtR)),6–11 or on comparisons of several anthropometric measures of obesity among select population subgroups.12 13 From public health and clinical monitoring perspectives, an important knowledge gap emerging from this work on trends in obesity across population subgroups is how anthropometric measures of obesity may vary with age cross-sectionally and longitudinally among middle-aged and older-aged African Americans (AAs) who experience a disproportionate burden of obesity-related health risks in the USA.14

    As AAs are known to have relatively less of their body mass in their trunks and relatively more in their extremities compared with non-Hispanic whites,5 a better understanding of variations in age-related and sex-related measures of adiposity among AA may suggest which anthropometric measure(s) may be better for monitoring obesity among elderly AA. In this study, we used data from the Jackson Heart Study (JHS), a community-based longitudinal study of cardiovascular diseases among adult AAs in Jackson, Mississippi, USA, to examine age-related and sex-related variations in several anthropometric measures of obesity including BMI, WC, WHtR and waist-to-hip ratio (WHR), both cross-sectionally and longitudinally.

    Methods

    Data source

    The JHS is the largest single-site, population-based cohort study of cardiovascular diseases in AAs. Participant recruitment for the JHS began in 2000, enrolling 5306 AAs from the tri-county area of the Jackson, Mississippi, USA, metropolitan area. Participants were examined at baseline (V1 2000–2004) and two additional examinations (V2 2005–2008 and V3 2009–2013). Additional details of the JHS study design, recruitment and data collection have been published previously.15–17

    Analytical sample

    Because WHR data were only available at V2 and V3, for comparison purposes, anthropometric measures from V2 were used for the cross-sectional analysis of the variations in anthropometric measures by age. Anthropometric measures from V2 and V3 were used for the longitudinal analysis of changes in anthropometric measures due to the ageing effect. All participants who returned for V2 were included (N=4205). Participants who had missing values of any of the anthropometric measures of interest at V2 (N=142) or covariates (N=429) (physical activity-4; smoking status-60; education-5; alcohol consumption-21; nutritional intake-339) were excluded, leaving 3634 participants in the analytical sample. Of the 3634 participants included in the analytical sample, 3141 returned for V3 (86.4% retention rate).

    Anthropometric measures of obesity

    The anthropometric measures of obesity included BMI, WC, WHtR and WHR. BMI was calculated as (weight (kg)/height2 (m2)).18 WHtR was calculated as WC (cm)/height (cm) and WHR was calculated as WC (cm)/hip circumference (cm). Weight was measured to the nearest 0.1 kg and height to the nearest centimetre in light clothing and in stocking feet; WC was measured to the nearest centimetre at the umbilicus; hip circumference was measured to the nearest centimetre at the maximal protrusion. Obesity status (obese/non-obese) was classified based on cut-points recommended by guidelines or reported in the literature for each obesity measure as follows: BMI ≥30 (kg/m2)19; WC >88 cm for women or >102 cm for men20 21; WHtR ≥0.522; WHR ≥0.85 for women or ≥0.9 for men.23 For comparison purposes, we computed sex-specific z-scores for each anthropometric measure to put them on the same scale, that is, in standardised (SD) unit, using the sex-specific means and SD from the analytical sample (all V2 and V3 measurements were pooled to calculate the sex-specific means and SD for each anthropometric measure).

    Covariates

    Covariates included education, physical activity, smoking status, alcohol consumption and nutritional intake collected at V1 since they were not collected at V2. Education was based on self-reported years of schooling completed and included three categories: less than high school (<12 years), high school graduate or General Educational Development (GED), and attended vocational school, trade school or college.

    Smoking status, physical activity and nutritional intake were self-reported. We used the American Heart Association (AHA) Life’s Simple 7 classification to classify these lifestyle factors for analysis. AHA Life’s Simple 7 is a metric developed by the AHA defining ideal cardiovascular health: not smoking, regular physical activity, healthy diet, maintaining normal weight and controlling cholesterol, blood pressure and blood glucose levels. We used its definitions of ‘poor health’, ‘intermediate health’ and ‘ideal health’ for controlling for lifestyle factors in the regression models. Smoking status was ‘poor’ if the participant was a current smoker; ‘intermediate’ if the participant had quit smoking less than a year prior to V1; ‘ideal’ if the participant had never smoked or had quit smoking more than a year prior to V1. Physical activity was ‘poor’ if the participant had 0 min of moderate (3.00–5.99 kcals/kg/hour) or vigorous (≥6.00 kcals/kg/hour) leisure activity per week; ‘intermediate’ if the participant did not have at least 150 min of moderate, or at least 75 min of vigorous, or at least 150 combined (moderate/vigorous) leisure activity per week; ‘ideal’ if the participant had at least 150 min of moderate, or at least 75 min of vigorous, or at least 150 min of combined moderate/vigorous leisure activity per week. Nutritional intake was assessed for all participants using the short Delta Nutrition Intervention Research Initiative Food Frequency Questionnaire with 158 items.24 The five dietary components used to compute the AHA score, based on a 2000 kcal diet, were (1) fruits and vegetables, ≥4.5 cups/day; (2) non-fried fish, ≥2 30.5-ounce servings/week; (3) fiber-rich whole grains, ≥3 1-ounce servings/day; (4) sodium, ≤1500 mg/day; and (5) sugar sweetened beverages, <36 fluid ounces/week (≤450 kcal/week). ‘Ideal’ diet was defined by a diet including 4–5 components; ‘intermediate’ diet, 2–3 components; and ‘poor’ diet, 0–1 component.25 Alcohol consumption was dichotomised (yes/no) per participant’s response to the question: ‘In the past 12 months, have you ever consumed an alcoholic beverage?’

    Data availability statement

    Requests for JHS data require approval of a JHS Manuscript Proposal or Ancillary Study Proposal. To protect the confidentiality and privacy of the JHS participants and their family, a Data and Materials Distribution Agreement is required to obtain data. To submit a request for data, complete a data request form at (https://redcap.umc.edu/surveys/?%20s=R48NR37HA8).26

    Statistical analysis

    Cross-sectional analysis

    Age at V2 was grouped into five age groups: <45, 45 to <55, 55 to <65, 65 to <75 and 75+ years to examine the cross-sectional effect of age on anthropometric measures of obesity. The adjusted means of anthropometric measures (in SD unit) by age group were calculated and compared using linear regression models. All regression models were adjusted for sex, education, physical activity, smoking status, alcohol consumption and nutritional intake. To examine whether the effect of age may be modified by sex, the significance of the sex×age group interaction term was tested by including the interaction term in the statistical models.

    Longitudinal analysis

    The longitudinal effect of age (ie, the effect of ageing) on anthropometric measures of obesity was examined by analysing the change in anthropometric measures (in SD unit) from V2 to V3 (V3 minus V2 measures) versus follow-up time between V2 and V3 (average 3.3 years, range 1.8–6.5 years). Follow-up time was rescaled as a 5-year increment so that the regression coefficient reflects a change across 5 years. All analyses were stratified by age group at V2 and sex, and performed using mixed effects models with random intercept (individuals) and random slope (follow-up time). All regression models were adjusted for age, education, physical activity, smoking status, alcohol consumption and nutritional intake. The significance of the modifying effect of sex was tested by including a sex×follow-up time interaction term in the statistical models.

    All reported p values correspond to two‐tailed tests and were significant at the 0.05 level. Analyses were performed using Stata/SE V.17.0.

    Participant involvement

    Participants who met eligibility requirements were asked to bring their family members for the family study, but they were not directly involved in recruitment or any other steps in this research process. Participants were sent lay summaries of the results of published manuscripts that derived from the JHS. Lay summaries of study findings were also disseminated to the general community and participants at JHS events.26

    Results

    Participant characteristics

    Table 1 shows the characteristics of participants in the analytical sample. The mean age of the participants was 59.4±12.0 years old (range 25–97) and about 65% were women. Over 85% of the participants had at least a high school education or GED. Compared with younger participants, older participants were more likely to be women, had less than high school education and reported lower levels of physical activity. Older participants were also more likely to have ideal smoking status and nutritional intake and never consumed alcohol beverages in the last 12 months.

    View this table:
    Table 1

    Characteristics of Jackson Heart Study participants included in the analytical sample

    The mean (SD) of anthropometric measures were as follows: weight 90.9 (21.0) kg, height 168.5 (9.4) cm, WC 102.0 (15.7) cm, hip circumference 114.2 (14.4) cm, BMI 32.0 (7.1) kg/m2, WHtR 0.61 (0.10) and WHR 0.89 (0.08). The prevalence of obesity defined by BMI, WC, WHtR and WHR were 55%, 68%, 91% and 64%, respectively. Compared with younger participants, anthropometric measures were generally lower in older participants, except for WHtR and WHR, which were higher in older participants. For WC, the largest WC was observed in the 55 to <65 and the 65 to <75 years groups and the smallest in the 75+ years group.

    For obesity, the prevalence of obesity increased with age when defined by WHtR (from 86% in the 25 to <45 years group to 94% in the 75+ years group) and WHR (from 48% in the 25 to <45 years old group to 78% in the 75+ years group). While the prevalence of obesity defined by BMI generally decreased with age from 63% in the 25 to <45 years group to 41% in the 75+ years group.

    Cross-sectional analysis

    Table 2 shows the results of the cross-sectional analysis of standardised anthropometric measures of obesity by age group from linear regression models adjusting for covariates. For BMI and WC, there was an inverse relationship with age, whereas WHR showed a positive relationship with age. For example, the adjusted mean BMI was 0.80 SD units lower in the oldest age group than in the youngest age group (β (95% CI) −0.80 (–0.94 to –0.66)). In contrast, the

    View this table:
    Table 2

    Cross-sectional analysis of standardised anthropometric measures at V2 by age group

    adjusted mean WHR was close to half SD unit higher in the oldest age group than in the youngest age group (β (95% CI) 0.48 (0.33 to 0.62)). The tests for trend for BMI, WC and WHR were statistically significant (p<0.05). No statistically significant sex×age interactions were observed (online supplemental table 1).

    Longitudinal analysis

    Figure 1 depicts the anthropometric data for longitudinal analysis by sex and age group (<45, 45 to <55, 55 to <65, 65 to <75 and 75+ years at V2). The mean anthropometric measures of obesity at V2 and V3 were calculated and plotted against the mean age at the respective visits. As shown in figure 1, the mean BMI and WHtR were higher in women than in men, while the mean WC and WHR were lower in women than in men across time and in all age groups.

    Figure 1
    Figure 1

    (A–D) Mean anthropometric measures values at V2 to V3 by age-group and sex. Each line shows the changes of unadjusted mean anthropometric measure from V2 to V3 for each age group stratified by sex. BMI, body mass index; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; yrs, years.

    Figure 2 shows the results of changes in standardised anthropometric measures of obesity per every 5 years increase in age (ageing) from mixed effects models adjusting for covariates, by sex and age group. A positive coefficient (ie, on the right side of the vertical reference line at ‘0’) indicates an increasing trend with ageing and a negative coefficient indicates a decreasing trend with ageing. As shown in figure 2A–C, ageing was associated with increased BMI, WC and WHtR among younger age groups, but the increasing trend tended to be diminished or reversed among older age groups in both sexes. For example, for BMI, 5-year ageing was associated with increase of 0.13 (95% CI 0.06 to 0.20) SD units in BMI in age groups 25 to <45 and 0.09 (95% CI 0.05 to 0.14) SD units in 45 to <55 years, but the trend reversed in age groups 65 years or older with decrease of −0.07 (95% CI –0.10 to –0.03) SD units in age group of 65 to <75 years and −0.14 (95% CI –0.19 to –0.09) SD units in 75+ years among women (figure 2A). On the other hand, WHR tended to increase with ageing across all sex and age groups, although the magnitude of the increasing trend generally decreased in older age groups (figure 2D). For instance, with each 5-year of ageing, the mean WHR increased by 0.74 (95% CI 0.52 to 0.97) SD units for 25 to <45 years, 0.51 (95% CI 0.39 to 0.62) SD units for 45 to <55 years, 0.50 (95% CI 0.38 to 0.62) SD units for 55 to <65 years, 0.47 (95% CI 0.34 to 0.59) SD units for 65 to <75 years and 0.24 (95% CI 0.05 to 0.44) SD units for 75+ years among men. Additionally, WHR had the highest SD unit increase with ageing among the four anthropometric measures of obesity examined. For instance, among men 45 years and younger, the mean WHR increased by 0.74 (95% CI 0.52 to 0.97) SD unit with each 5-year of ageing, while the mean BMI, WC and WHtR increased only by 0.14 (95% CI 0.05 to 0.23), 0.28 (95% CI 0.16 to 0.40) and 0.30 (95% CI 0.17 to 0.42) SD unit, respectively. Statistically significant sex×age interactions were observed in the analysis for WHR among the 25 to <45 years group and WC among the 45 to <55 years group (online supplemental table 2) although they were in the same direction, but different magnitudes of the increasing trend among different sexes (figure 2).

    Figure 2
    Figure 2

    (A–D) Longitudinal analyses of changes of standardised anthropometric measures per 5-year ageing by sex and age group. Mixed effects models with random intercept and random slope were used to test ageing effect. Models were adjusted for covariates collected at V1, including age, education, physical activity, smoking status, alcohol consumption and nutritional intake. BMI, body mass index; F, female; M, male; yrs, years.

    Discussion

    In this study of a large sample of adult AA men and women in Jackson, Mississippi, USA, we found that in cross-sectional analyses, compared with the younger age group, the older age group had a lower mean BMI, WC and WHtR, but a higher mean WHR. In longitudinal analyses, the effect of ageing on the changes in BMI, WC and WHtR was similar with the mean values increasing with age only among younger groups. In contrast, the mean WHR increased with age across all age groups.

    The observation that BMI, WC and WHtR decreased with age while WHR increased with age in the older age group suggests that loss of lean muscle mass or change in the distribution of fat and lean mass with age may play an important role in disease risks associated with obesity in older adults. This may also partially explain why older adults considered to be overweight based on BMI have a lower mortality rate than those with lower BMI (the ‘obesity paradox’).27 In a prospective cohort study among male health professionals in the USA, a significant positive monotonic association between predicted fat mass and all-cause mortality, and a U-shaped association between predicted lean body mass and all-cause mortality were found, suggesting that the ‘obesity paradox’ may be largely caused by low lean body mass, instead of low-fat mass, in the lower BMI range.28 In another prospective cohort study of older British men, it was found that sarcopenia and central adiposity were associated with higher all-cause mortality.29 In our own analyses in the JHS, we found the relationships between BMI, WC, WHtR and overall mortality to be J-shaped whereas there was a monotonic increasing relationship between WHR and overall mortality, suggesting once corrected for gluteal muscle mass, lower BMI or central adiposity does not afford greater mortality risks.30 Ageing is related to muscle loss and visceral fat accumulation, and has been associated with several cardiometabolic chronic diseases and mortality.31 Therefore, anthropometric measures and ageing are together key risk factors of chronic diseases and mortality. This analyses further reveal the relationship between the two risk factors, and explains the rationale behind the relationships among risk factors and mortality in AA adults. These findings with WHR fit well with the postulated biological changes in old age, including redistribution of adiposity from limbs to visceral with concomitant loss of lean muscle mass, as approximated by waist circumference (central adiposity) and hip circumferences (gluteal muscle), respectively.23 Therefore, WHR has been reported to be a better predictor of obesity-related risk among older adults than BMI and WC.32 33

    Reviewers of an earlier version of this manuscript suggested that we include comorbid conditions as covariates in our analysis. We have opted not to adjust for comorbidity because comorbidity, in our opinion, does not meet the definition of confounder as it does not cause or prevent obesity. Rather, comorbidity may be caused in part by old age/ageing (the exposure) and is also correlated with obesity, thus adjusting for it may introduce bias.34 Furthermore, given that our objective is to describe the variation of obesity with age and possibly identify a better measure to monitor obesity (ie, not to explain it), adjusting for morbidity associated with obesity that may potentially mask the relationship between age and obesity would seem to defeat our purpose.

    The strengths of this study include the large sample size and longitudinal follow-up of a well-characterised cohort of adult AAs, including a substantial proportion of elderly men and women with anthropometric measures of obesity across two visits, and a relatively high retention rate (86%) across visits. However, this study has several limitations. First, the longitudinal change in anthropometric measures over the life-course were derived from participants from different birth cohorts. While it is reasonable to assume that the longitudinal trend observed in anthropometric measures may be consistent with the biology of ageing irrespective of birth cohort, we cannot be certain without longer-term follow-up of participants of the same birth cohort. Furthermore, the anthropometric measures from V2 and V3 were used for the analysis, while the covariates including education, physical activity, smoking status, alcohol consumption and nutritional intake collected at V1 were used for the analysis since they were not collected at V2. In addition, the results may not be generalisable to all AA adults across the USA, as the sample was drawn solely from residents of Jackson, Mississippi, USA.

    Conclusion

    Our results showed that among middle-aged AA adults, all four anthropometric measures of obesity examined (BMI, WC, WHtR and WHR) increased with ageing. However, among elderly AA adults, WHR was the only anthropometric measure that showed continued increase with ageing. Our findings suggest that WHR, a measure that captures both central adiposity and body composition, may be an important anthropometric measure to collect to monitor obesity and obesity-related health risks among older AA adults. These findings should be verified in other ethnically diverse populations.

    Data availability statement

    Data are available upon reasonable request. As a National Heart, Lung, and Blood Institute (NHLBI)-funded study, the Jackson Heart Study (JHS) follows the NHLBI’s policy for data sharing, which includes depositing the data into the NHLBI’s Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) to make it publicly available to other investigators. The link to the JHS data set: https://biolincc-nhlbi-nih-gov.ezproxy.u-pec.fr/studies/jhs/. Download directions are provided on the website. NHLBI further requires the JHS to maintain the accuracy of this data set, so the JHS Coordinating Center sends modifications to BioLINCC for incorporation as appropriate. Privacy and access settings are controlled by BioLINCC, and the investigators have no influence on these settings.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The study was approved by the Institutional Review Board of the University of Mississippi Medical Center (UMMC IRB Tracking Number: 1998-6004; DHHS FWA: 00003630 0000043; IORG: 00000061; IRB Registration: 00005033) and participants provided written informed consent. The Jackson Heart Study is an observational cohort study. No experiments were performed in the study. Participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    The authors wish to thank the participants and data collection staff of the Jackson Heart Study.

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    Supplementary materials

    • Supplementary Data

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    Footnotes

    • YG and Y-IM are joint first authors.

    • Contributors YG: analysis and interpretation of data, drafting and final approval of the manuscript and being responsible for the overall content as the guarantor. Y-IM: statistical expertise, helped conduct the literature review, critical review and final approval of the manuscript. RAB: critical review and final approval of the manuscript. AGB: critical review and final approval of the manuscript. AC: conception and design of study, critical review and final approval of manuscript. All authors have read and approved the manuscript.

    • Funding The Jackson Heart Study (JHS) is supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD) and is conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I).

    • Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institute for Minority Health and Health Disparities; the National Institutes of Health; or the US Department of Health and Human Services.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.